Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations200000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory293.9 MiB
Average record size in memory1.5 KiB

Variable types

Text10
Categorical13
Numeric3
DateTime1

Alerts

Transaction_Currency has constant value "INR" Constant
Transaction_year has constant value "2025" Constant
Transaction_month has constant value "January" Constant
Is_Fraud is highly imbalanced (71.2%) Imbalance
Customer_ID has unique values Unique
Transaction_ID has unique values Unique
Merchant_ID has unique values Unique

Reproduction

Analysis started2025-02-20 04:43:43.999932
Analysis finished2025-02-20 04:44:21.385164
Duration37.39 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Customer_ID
Text

Unique 

Distinct200000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.7 MiB
2025-02-20T04:44:21.874214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters7200000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200000 ?
Unique (%)100.0%

Sample

1st rowd5f6ec07-d69e-4f47-b9b4-7c58ff17c19e
2nd row7c14ad51-781a-4db9-b7bd-67439c175262
3rd row3a73a0e5-d4da-45aa-85f3-528413900a35
4th row7902f4ef-9050-4a79-857d-9c2ea3181940
5th row3a4bba70-d9a9-4c5f-8b92-1735fd8c19e9
ValueCountFrequency (%)
d5f6ec07-d69e-4f47-b9b4-7c58ff17c19e 1
 
< 0.1%
fcf401c6-f4ca-4e7d-a983-f3fb6d05c1a1 1
 
< 0.1%
ef96e796-8b05-47d1-b77d-91651f75e0d4 1
 
< 0.1%
d4d0f62e-00b0-4c0e-8188-0b5a3f3e6c3e 1
 
< 0.1%
3a73a0e5-d4da-45aa-85f3-528413900a35 1
 
< 0.1%
7902f4ef-9050-4a79-857d-9c2ea3181940 1
 
< 0.1%
3a4bba70-d9a9-4c5f-8b92-1735fd8c19e9 1
 
< 0.1%
6c870d65-76b0-431d-bdf3-9292998e8211 1
 
< 0.1%
5323737c-bbd2-423f-9c9b-e0433c8f75dc 1
 
< 0.1%
c0c3d474-f6c2-4c66-9b0e-f9ba75c6f310 1
 
< 0.1%
Other values (199990) 199990
> 99.9%
2025-02-20T04:44:22.445453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 800000
 
11.1%
4 574973
 
8.0%
a 425432
 
5.9%
9 424370
 
5.9%
b 424292
 
5.9%
8 423831
 
5.9%
e 376040
 
5.2%
7 375884
 
5.2%
5 375571
 
5.2%
f 375331
 
5.2%
Other values (7) 2624276
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 800000
 
11.1%
4 574973
 
8.0%
a 425432
 
5.9%
9 424370
 
5.9%
b 424292
 
5.9%
8 423831
 
5.9%
e 376040
 
5.2%
7 375884
 
5.2%
5 375571
 
5.2%
f 375331
 
5.2%
Other values (7) 2624276
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 800000
 
11.1%
4 574973
 
8.0%
a 425432
 
5.9%
9 424370
 
5.9%
b 424292
 
5.9%
8 423831
 
5.9%
e 376040
 
5.2%
7 375884
 
5.2%
5 375571
 
5.2%
f 375331
 
5.2%
Other values (7) 2624276
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 800000
 
11.1%
4 574973
 
8.0%
a 425432
 
5.9%
9 424370
 
5.9%
b 424292
 
5.9%
8 423831
 
5.9%
e 376040
 
5.2%
7 375884
 
5.2%
5 375571
 
5.2%
f 375331
 
5.2%
Other values (7) 2624276
36.4%
Distinct142699
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
2025-02-20T04:44:22.775663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length24
Mean length12.53347
Min length5

Characters and Unicode

Total characters2506694
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98022 ?
Unique (%)49.0%

Sample

1st rowOsha Tella
2nd rowHredhaan Khosla
3rd rowEkani Nazareth
4th rowYamini Ramachandran
5th rowKritika Rege
ValueCountFrequency (%)
kala 1408
 
0.4%
krishna 1101
 
0.3%
sai 768
 
0.2%
bora 765
 
0.2%
gopal 760
 
0.2%
palla 756
 
0.2%
krish 753
 
0.2%
wali 752
 
0.2%
madan 746
 
0.2%
om 738
 
0.2%
Other values (1050) 391453
97.9%
2025-02-20T04:44:23.311171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 502429
20.0%
200000
 
8.0%
i 176547
 
7.0%
h 157460
 
6.3%
r 150363
 
6.0%
n 147918
 
5.9%
e 93447
 
3.7%
t 81898
 
3.3%
l 73798
 
2.9%
s 72871
 
2.9%
Other values (44) 849963
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2506694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 502429
20.0%
200000
 
8.0%
i 176547
 
7.0%
h 157460
 
6.3%
r 150363
 
6.0%
n 147918
 
5.9%
e 93447
 
3.7%
t 81898
 
3.3%
l 73798
 
2.9%
s 72871
 
2.9%
Other values (44) 849963
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2506694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 502429
20.0%
200000
 
8.0%
i 176547
 
7.0%
h 157460
 
6.3%
r 150363
 
6.0%
n 147918
 
5.9%
e 93447
 
3.7%
t 81898
 
3.3%
l 73798
 
2.9%
s 72871
 
2.9%
Other values (44) 849963
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2506694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 502429
20.0%
200000
 
8.0%
i 176547
 
7.0%
h 157460
 
6.3%
r 150363
 
6.0%
n 147918
 
5.9%
e 93447
 
3.7%
t 81898
 
3.3%
l 73798
 
2.9%
s 72871
 
2.9%
Other values (44) 849963
33.9%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.8 MiB
Male
100452 
Female
99548 

Length

Max length6
Median length4
Mean length4.99548
Min length4

Characters and Unicode

Total characters999096
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 100452
50.2%
Female 99548
49.8%

Length

2025-02-20T04:44:23.467457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T04:44:23.572532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 100452
50.2%
female 99548
49.8%

Most occurring characters

ValueCountFrequency (%)
e 299548
30.0%
a 200000
20.0%
l 200000
20.0%
M 100452
 
10.1%
F 99548
 
10.0%
m 99548
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 999096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 299548
30.0%
a 200000
20.0%
l 200000
20.0%
M 100452
 
10.1%
F 99548
 
10.0%
m 99548
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 999096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 299548
30.0%
a 200000
20.0%
l 200000
20.0%
M 100452
 
10.1%
F 99548
 
10.0%
m 99548
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 999096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 299548
30.0%
a 200000
20.0%
l 200000
20.0%
M 100452
 
10.1%
F 99548
 
10.0%
m 99548
 
10.0%

Age
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.01511
Minimum18
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-02-20T04:44:23.717488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q131
median44
Q357
95-th percentile68
Maximum70
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.288774
Coefficient of variation (CV)0.34735286
Kurtosis-1.2002993
Mean44.01511
Median Absolute Deviation (MAD)13
Skewness0.0026443514
Sum8803022
Variance233.74662
MonotonicityNot monotonic
2025-02-20T04:44:23.920619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 3929
 
2.0%
42 3871
 
1.9%
70 3864
 
1.9%
39 3858
 
1.9%
32 3857
 
1.9%
56 3841
 
1.9%
69 3840
 
1.9%
23 3832
 
1.9%
37 3829
 
1.9%
30 3823
 
1.9%
Other values (43) 161456
80.7%
ValueCountFrequency (%)
18 3712
1.9%
19 3796
1.9%
20 3644
1.8%
21 3691
1.8%
22 3722
1.9%
23 3832
1.9%
24 3820
1.9%
25 3804
1.9%
26 3814
1.9%
27 3673
1.8%
ValueCountFrequency (%)
70 3864
1.9%
69 3840
1.9%
68 3775
1.9%
67 3700
1.8%
66 3738
1.9%
65 3761
1.9%
64 3676
1.8%
63 3766
1.9%
62 3804
1.9%
61 3929
2.0%

State
Categorical

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
Nagaland
 
6031
Meghalaya
 
6003
Uttar Pradesh
 
6002
Uttarakhand
 
5985
Lakshadweep
 
5954
Other values (29)
170025 

Length

Max length40
Median length16
Mean length10.491565
Min length3

Characters and Unicode

Total characters2098313
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKerala
2nd rowMaharashtra
3rd rowBihar
4th rowTamil Nadu
5th rowPunjab

Common Values

ValueCountFrequency (%)
Nagaland 6031
 
3.0%
Meghalaya 6003
 
3.0%
Uttar Pradesh 6002
 
3.0%
Uttarakhand 5985
 
3.0%
Lakshadweep 5954
 
3.0%
Telangana 5952
 
3.0%
Haryana 5947
 
3.0%
Delhi 5943
 
3.0%
Kerala 5933
 
3.0%
Madhya Pradesh 5928
 
3.0%
Other values (24) 140322
70.2%

Length

2025-02-20T04:44:24.125487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 29497
 
9.8%
and 23403
 
7.8%
nagaland 6031
 
2.0%
meghalaya 6003
 
2.0%
uttar 6002
 
2.0%
uttarakhand 5985
 
2.0%
lakshadweep 5954
 
2.0%
telangana 5952
 
2.0%
haryana 5947
 
2.0%
delhi 5943
 
2.0%
Other values (34) 198967
66.4%

Most occurring characters

ValueCountFrequency (%)
a 452941
21.6%
r 164371
 
7.8%
h 152668
 
7.3%
d 129355
 
6.2%
n 129212
 
6.2%
99684
 
4.8%
e 88623
 
4.2%
s 87894
 
4.2%
i 87792
 
4.2%
l 65037
 
3.1%
Other values (33) 640736
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2098313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 452941
21.6%
r 164371
 
7.8%
h 152668
 
7.3%
d 129355
 
6.2%
n 129212
 
6.2%
99684
 
4.8%
e 88623
 
4.2%
s 87894
 
4.2%
i 87792
 
4.2%
l 65037
 
3.1%
Other values (33) 640736
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2098313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 452941
21.6%
r 164371
 
7.8%
h 152668
 
7.3%
d 129355
 
6.2%
n 129212
 
6.2%
99684
 
4.8%
e 88623
 
4.2%
s 87894
 
4.2%
i 87792
 
4.2%
l 65037
 
3.1%
Other values (33) 640736
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2098313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 452941
21.6%
r 164371
 
7.8%
h 152668
 
7.3%
d 129355
 
6.2%
n 129212
 
6.2%
99684
 
4.8%
e 88623
 
4.2%
s 87894
 
4.2%
i 87792
 
4.2%
l 65037
 
3.1%
Other values (33) 640736
30.5%

City
Text

Distinct145
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
2025-02-20T04:44:24.472443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length11
Mean length7.587175
Min length3

Characters and Unicode

Total characters1517435
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThiruvananthapuram
2nd rowNashik
3rd rowBhagalpur
4th rowChennai
5th rowAmritsar
ValueCountFrequency (%)
chandigarh 8135
 
3.8%
kavaratti 5954
 
2.8%
delhi 5943
 
2.8%
udaipur 2681
 
1.3%
daman 2022
 
1.0%
nicobar 1956
 
0.9%
car 1956
 
0.9%
blair 1950
 
0.9%
port 1950
 
0.9%
diglipur 1926
 
0.9%
Other values (140) 178344
83.8%
2025-02-20T04:44:24.982185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 287153
18.9%
r 130021
 
8.6%
i 107482
 
7.1%
h 86167
 
5.7%
u 77144
 
5.1%
n 73354
 
4.8%
o 62062
 
4.1%
l 61412
 
4.0%
g 54409
 
3.6%
t 49470
 
3.3%
Other values (38) 528761
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1517435
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 287153
18.9%
r 130021
 
8.6%
i 107482
 
7.1%
h 86167
 
5.7%
u 77144
 
5.1%
n 73354
 
4.8%
o 62062
 
4.1%
l 61412
 
4.0%
g 54409
 
3.6%
t 49470
 
3.3%
Other values (38) 528761
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1517435
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 287153
18.9%
r 130021
 
8.6%
i 107482
 
7.1%
h 86167
 
5.7%
u 77144
 
5.1%
n 73354
 
4.8%
o 62062
 
4.1%
l 61412
 
4.0%
g 54409
 
3.6%
t 49470
 
3.3%
Other values (38) 528761
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1517435
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 287153
18.9%
r 130021
 
8.6%
i 107482
 
7.1%
h 86167
 
5.7%
u 77144
 
5.1%
n 73354
 
4.8%
o 62062
 
4.1%
l 61412
 
4.0%
g 54409
 
3.6%
t 49470
 
3.3%
Other values (38) 528761
34.8%
Distinct145
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.7 MiB
2025-02-20T04:44:25.347327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length18
Mean length14.587175
Min length10

Characters and Unicode

Total characters2917435
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThiruvananthapuram Branch
2nd rowNashik Branch
3rd rowBhagalpur Branch
4th rowChennai Branch
5th rowAmritsar Branch
ValueCountFrequency (%)
branch 200000
48.4%
chandigarh 8135
 
2.0%
kavaratti 5954
 
1.4%
delhi 5943
 
1.4%
udaipur 2681
 
0.6%
daman 2022
 
0.5%
car 1956
 
0.5%
nicobar 1956
 
0.5%
port 1950
 
0.5%
blair 1950
 
0.5%
Other values (141) 180270
43.7%
2025-02-20T04:44:25.889832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 487153
16.7%
r 330021
11.3%
h 286167
9.8%
n 273354
9.4%
c 217565
 
7.5%
212817
 
7.3%
B 212548
 
7.3%
i 107482
 
3.7%
u 77144
 
2.6%
o 62062
 
2.1%
Other values (38) 651122
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2917435
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 487153
16.7%
r 330021
11.3%
h 286167
9.8%
n 273354
9.4%
c 217565
 
7.5%
212817
 
7.3%
B 212548
 
7.3%
i 107482
 
3.7%
u 77144
 
2.6%
o 62062
 
2.1%
Other values (38) 651122
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2917435
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 487153
16.7%
r 330021
11.3%
h 286167
9.8%
n 273354
9.4%
c 217565
 
7.5%
212817
 
7.3%
B 212548
 
7.3%
i 107482
 
3.7%
u 77144
 
2.6%
o 62062
 
2.1%
Other values (38) 651122
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2917435
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 487153
16.7%
r 330021
11.3%
h 286167
9.8%
n 273354
9.4%
c 217565
 
7.5%
212817
 
7.3%
B 212548
 
7.3%
i 107482
 
3.7%
u 77144
 
2.6%
o 62062
 
2.1%
Other values (38) 651122
22.3%

Account_Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
Checking
66924 
Savings
66593 
Business
66483 

Length

Max length8
Median length8
Mean length7.667035
Min length7

Characters and Unicode

Total characters1533407
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSavings
2nd rowBusiness
3rd rowSavings
4th rowBusiness
5th rowSavings

Common Values

ValueCountFrequency (%)
Checking 66924
33.5%
Savings 66593
33.3%
Business 66483
33.2%

Length

2025-02-20T04:44:26.074110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T04:44:26.184884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
checking 66924
33.5%
savings 66593
33.3%
business 66483
33.2%

Most occurring characters

ValueCountFrequency (%)
s 266042
17.3%
i 200000
13.0%
n 200000
13.0%
g 133517
8.7%
e 133407
8.7%
C 66924
 
4.4%
h 66924
 
4.4%
c 66924
 
4.4%
k 66924
 
4.4%
S 66593
 
4.3%
Other values (4) 266152
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1533407
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 266042
17.3%
i 200000
13.0%
n 200000
13.0%
g 133517
8.7%
e 133407
8.7%
C 66924
 
4.4%
h 66924
 
4.4%
c 66924
 
4.4%
k 66924
 
4.4%
S 66593
 
4.3%
Other values (4) 266152
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1533407
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 266042
17.3%
i 200000
13.0%
n 200000
13.0%
g 133517
8.7%
e 133407
8.7%
C 66924
 
4.4%
h 66924
 
4.4%
c 66924
 
4.4%
k 66924
 
4.4%
S 66593
 
4.3%
Other values (4) 266152
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1533407
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 266042
17.3%
i 200000
13.0%
n 200000
13.0%
g 133517
8.7%
e 133407
8.7%
C 66924
 
4.4%
h 66924
 
4.4%
c 66924
 
4.4%
k 66924
 
4.4%
S 66593
 
4.3%
Other values (4) 266152
17.4%

Transaction_ID
Text

Unique 

Distinct200000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.7 MiB
2025-02-20T04:44:26.600131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters7200000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200000 ?
Unique (%)100.0%

Sample

1st row4fa3208f-9e23-42dc-b330-844829d0c12c
2nd rowc9de0c06-2c4c-40a9-97ed-3c7b8f97c79c
3rd rowe41c55f9-c016-4ff3-872b-cae72467c75c
4th row7f7ee11b-ff2c-45a3-802a-49bc47c02ecb
5th rowf8e6ac6f-81a1-4985-bf12-f60967d852ef
ValueCountFrequency (%)
4fa3208f-9e23-42dc-b330-844829d0c12c 1
 
< 0.1%
95c868ae-56fc-481a-a973-a253893ebe32 1
 
< 0.1%
fa215182-59bb-4fb0-80a9-fabd454b4a07 1
 
< 0.1%
9a03bbb6-0b7b-4ea6-919f-ba430637b612 1
 
< 0.1%
e41c55f9-c016-4ff3-872b-cae72467c75c 1
 
< 0.1%
7f7ee11b-ff2c-45a3-802a-49bc47c02ecb 1
 
< 0.1%
f8e6ac6f-81a1-4985-bf12-f60967d852ef 1
 
< 0.1%
af5f667c-d064-4083-bfb7-83396111a3da 1
 
< 0.1%
b1355810-d246-4aeb-9932-347f32646172 1
 
< 0.1%
c86a000c-d81f-40be-acdf-77fc072fd808 1
 
< 0.1%
Other values (199990) 199990
> 99.9%
2025-02-20T04:44:27.172938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 800000
 
11.1%
4 575390
 
8.0%
a 425697
 
5.9%
8 425119
 
5.9%
9 424879
 
5.9%
b 424672
 
5.9%
6 375916
 
5.2%
d 375659
 
5.2%
2 375305
 
5.2%
1 375249
 
5.2%
Other values (7) 2622114
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 800000
 
11.1%
4 575390
 
8.0%
a 425697
 
5.9%
8 425119
 
5.9%
9 424879
 
5.9%
b 424672
 
5.9%
6 375916
 
5.2%
d 375659
 
5.2%
2 375305
 
5.2%
1 375249
 
5.2%
Other values (7) 2622114
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 800000
 
11.1%
4 575390
 
8.0%
a 425697
 
5.9%
8 425119
 
5.9%
9 424879
 
5.9%
b 424672
 
5.9%
6 375916
 
5.2%
d 375659
 
5.2%
2 375305
 
5.2%
1 375249
 
5.2%
Other values (7) 2622114
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 800000
 
11.1%
4 575390
 
8.0%
a 425697
 
5.9%
8 425119
 
5.9%
9 424879
 
5.9%
b 424672
 
5.9%
6 375916
 
5.2%
d 375659
 
5.2%
2 375305
 
5.2%
1 375249
 
5.2%
Other values (7) 2622114
36.4%
Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2025-01-01 00:00:00
Maximum2025-01-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-20T04:44:27.314715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T04:44:27.499136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)

Transaction_Time
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 MiB
Night
66568 
Morning
58489 
Afternoon
41835 
Evening
33108 

Length

Max length9
Median length7
Mean length6.75267
Min length5

Characters and Unicode

Total characters1350534
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfternoon
2nd rowEvening
3rd rowNight
4th rowAfternoon
5th rowEvening

Common Values

ValueCountFrequency (%)
Night 66568
33.3%
Morning 58489
29.2%
Afternoon 41835
20.9%
Evening 33108
16.6%

Length

2025-02-20T04:44:27.667141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T04:44:27.790753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
night 66568
33.3%
morning 58489
29.2%
afternoon 41835
20.9%
evening 33108
16.6%

Most occurring characters

ValueCountFrequency (%)
n 266864
19.8%
i 158165
11.7%
g 158165
11.7%
o 142159
10.5%
t 108403
8.0%
r 100324
 
7.4%
e 74943
 
5.5%
N 66568
 
4.9%
h 66568
 
4.9%
M 58489
 
4.3%
Other values (4) 149886
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1350534
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 266864
19.8%
i 158165
11.7%
g 158165
11.7%
o 142159
10.5%
t 108403
8.0%
r 100324
 
7.4%
e 74943
 
5.5%
N 66568
 
4.9%
h 66568
 
4.9%
M 58489
 
4.3%
Other values (4) 149886
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1350534
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 266864
19.8%
i 158165
11.7%
g 158165
11.7%
o 142159
10.5%
t 108403
8.0%
r 100324
 
7.4%
e 74943
 
5.5%
N 66568
 
4.9%
h 66568
 
4.9%
M 58489
 
4.3%
Other values (4) 149886
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1350534
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 266864
19.8%
i 158165
11.7%
g 158165
11.7%
o 142159
10.5%
t 108403
8.0%
r 100324
 
7.4%
e 74943
 
5.5%
N 66568
 
4.9%
h 66568
 
4.9%
M 58489
 
4.3%
Other values (4) 149886
11.1%

Transaction_Amount
Real number (ℝ)

Distinct197978
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49538.016
Minimum10.29
Maximum98999.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-02-20T04:44:27.961717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.29
5-th percentile4982.469
Q124851.345
median49502.44
Q374314.625
95-th percentile93992.851
Maximum98999.98
Range98989.69
Interquartile range (IQR)49463.28

Descriptive statistics

Standard deviation28551.874
Coefficient of variation (CV)0.57636289
Kurtosis-1.1981949
Mean49538.016
Median Absolute Deviation (MAD)24736.87
Skewness-0.00047189794
Sum9.9076031 × 109
Variance8.1520951 × 108
MonotonicityNot monotonic
2025-02-20T04:44:28.193910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78931.12 3
 
< 0.1%
48165.03 3
 
< 0.1%
3591.91 3
 
< 0.1%
56984.73 3
 
< 0.1%
60214.31 3
 
< 0.1%
19882.01 3
 
< 0.1%
72214.37 3
 
< 0.1%
23307.61 3
 
< 0.1%
80360.67 3
 
< 0.1%
7771.5 3
 
< 0.1%
Other values (197968) 199970
> 99.9%
ValueCountFrequency (%)
10.29 1
< 0.1%
10.41 1
< 0.1%
10.52 1
< 0.1%
11.47 1
< 0.1%
12.61 1
< 0.1%
12.86 1
< 0.1%
14.43 1
< 0.1%
14.48 1
< 0.1%
15.11 1
< 0.1%
15.52 1
< 0.1%
ValueCountFrequency (%)
98999.98 1
< 0.1%
98999.45 1
< 0.1%
98999.02 1
< 0.1%
98997.99 1
< 0.1%
98997.02 1
< 0.1%
98997.01 1
< 0.1%
98995.96 1
< 0.1%
98995.92 1
< 0.1%
98995.74 1
< 0.1%
98995.42 1
< 0.1%

Merchant_ID
Text

Unique 

Distinct200000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.7 MiB
2025-02-20T04:44:28.891079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters7200000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200000 ?
Unique (%)100.0%

Sample

1st row214e03c5-5c34-40d1-a66c-f440aa2bbd02
2nd rowf9e3f11f-28d3-4199-b0ca-f225a155ede6
3rd row97977d83-5486-4510-af1c-8dada3e1cfa0
4th rowf45cd6b3-5092-44d0-8afb-490894605184
5th row70dd77dd-3b00-4b2c-8ebc-cfb8af5f6741
ValueCountFrequency (%)
214e03c5-5c34-40d1-a66c-f440aa2bbd02 1
 
< 0.1%
5ff3af4e-f552-4b21-8884-b698aa6fc231 1
 
< 0.1%
22537967-e926-4240-b643-93c2b7be2d2b 1
 
< 0.1%
3777c223-bf6f-443f-8964-5f60155cca98 1
 
< 0.1%
97977d83-5486-4510-af1c-8dada3e1cfa0 1
 
< 0.1%
f45cd6b3-5092-44d0-8afb-490894605184 1
 
< 0.1%
70dd77dd-3b00-4b2c-8ebc-cfb8af5f6741 1
 
< 0.1%
d82a798d-7b4d-4609-a687-8f6b5fc58fe7 1
 
< 0.1%
e0b802ba-b1d9-4e94-9a88-6fbb6066b4e0 1
 
< 0.1%
ec344461-1bab-4e85-a1cb-05061e9149bc 1
 
< 0.1%
Other values (199990) 199990
> 99.9%
2025-02-20T04:44:29.774572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 800000
 
11.1%
4 573909
 
8.0%
9 425368
 
5.9%
a 424987
 
5.9%
b 424654
 
5.9%
8 424387
 
5.9%
0 376502
 
5.2%
2 375892
 
5.2%
c 375759
 
5.2%
e 375103
 
5.2%
Other values (7) 2623439
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 800000
 
11.1%
4 573909
 
8.0%
9 425368
 
5.9%
a 424987
 
5.9%
b 424654
 
5.9%
8 424387
 
5.9%
0 376502
 
5.2%
2 375892
 
5.2%
c 375759
 
5.2%
e 375103
 
5.2%
Other values (7) 2623439
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 800000
 
11.1%
4 573909
 
8.0%
9 425368
 
5.9%
a 424987
 
5.9%
b 424654
 
5.9%
8 424387
 
5.9%
0 376502
 
5.2%
2 375892
 
5.2%
c 375759
 
5.2%
e 375103
 
5.2%
Other values (7) 2623439
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 800000
 
11.1%
4 573909
 
8.0%
9 425368
 
5.9%
a 424987
 
5.9%
b 424654
 
5.9%
8 424387
 
5.9%
0 376502
 
5.2%
2 375892
 
5.2%
c 375759
 
5.2%
e 375103
 
5.2%
Other values (7) 2623439
36.4%

Transaction_Type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.4 MiB
Credit
40180 
Debit
40050 
Bill Payment
40040 
Transfer
39953 
Withdrawal
39777 

Length

Max length12
Median length8
Mean length8.19602
Min length5

Characters and Unicode

Total characters1639204
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransfer
2nd rowBill Payment
3rd rowBill Payment
4th rowDebit
5th rowTransfer

Common Values

ValueCountFrequency (%)
Credit 40180
20.1%
Debit 40050
20.0%
Bill Payment 40040
20.0%
Transfer 39953
20.0%
Withdrawal 39777
19.9%

Length

2025-02-20T04:44:29.929216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T04:44:30.107515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
credit 40180
16.7%
debit 40050
16.7%
bill 40040
16.7%
payment 40040
16.7%
transfer 39953
16.6%
withdrawal 39777
16.6%

Most occurring characters

ValueCountFrequency (%)
e 160223
 
9.8%
i 160047
 
9.8%
t 160047
 
9.8%
r 159863
 
9.8%
a 159547
 
9.7%
l 119857
 
7.3%
n 79993
 
4.9%
d 79957
 
4.9%
C 40180
 
2.5%
D 40050
 
2.4%
Other values (12) 479440
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1639204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 160223
 
9.8%
i 160047
 
9.8%
t 160047
 
9.8%
r 159863
 
9.8%
a 159547
 
9.7%
l 119857
 
7.3%
n 79993
 
4.9%
d 79957
 
4.9%
C 40180
 
2.5%
D 40050
 
2.4%
Other values (12) 479440
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1639204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 160223
 
9.8%
i 160047
 
9.8%
t 160047
 
9.8%
r 159863
 
9.8%
a 159547
 
9.7%
l 119857
 
7.3%
n 79993
 
4.9%
d 79957
 
4.9%
C 40180
 
2.5%
D 40050
 
2.4%
Other values (12) 479440
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1639204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 160223
 
9.8%
i 160047
 
9.8%
t 160047
 
9.8%
r 159863
 
9.8%
a 159547
 
9.7%
l 119857
 
7.3%
n 79993
 
4.9%
d 79957
 
4.9%
C 40180
 
2.5%
D 40050
 
2.4%
Other values (12) 479440
29.2%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
Restaurant
33525 
Entertainment
33421 
Electronics
33409 
Clothing
33340 
Groceries
33187 

Length

Max length13
Median length10
Mean length9.506665
Min length6

Characters and Unicode

Total characters1901333
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRestaurant
2nd rowRestaurant
3rd rowGroceries
4th rowEntertainment
5th rowEntertainment

Common Values

ValueCountFrequency (%)
Restaurant 33525
16.8%
Entertainment 33421
16.7%
Electronics 33409
16.7%
Clothing 33340
16.7%
Groceries 33187
16.6%
Health 33118
16.6%

Length

2025-02-20T04:44:30.307938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T04:44:30.454801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
restaurant 33525
16.8%
entertainment 33421
16.7%
electronics 33409
16.7%
clothing 33340
16.7%
groceries 33187
16.6%
health 33118
16.6%

Most occurring characters

ValueCountFrequency (%)
t 267180
14.1%
e 233268
12.3%
n 200537
10.5%
r 166729
8.8%
a 133589
 
7.0%
i 133357
 
7.0%
s 100121
 
5.3%
c 100005
 
5.3%
o 99936
 
5.3%
l 99867
 
5.3%
Other values (9) 366744
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1901333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 267180
14.1%
e 233268
12.3%
n 200537
10.5%
r 166729
8.8%
a 133589
 
7.0%
i 133357
 
7.0%
s 100121
 
5.3%
c 100005
 
5.3%
o 99936
 
5.3%
l 99867
 
5.3%
Other values (9) 366744
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1901333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 267180
14.1%
e 233268
12.3%
n 200537
10.5%
r 166729
8.8%
a 133589
 
7.0%
i 133357
 
7.0%
s 100121
 
5.3%
c 100005
 
5.3%
o 99936
 
5.3%
l 99867
 
5.3%
Other values (9) 366744
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1901333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 267180
14.1%
e 233268
12.3%
n 200537
10.5%
r 166729
8.8%
a 133589
 
7.0%
i 133357
 
7.0%
s 100121
 
5.3%
c 100005
 
5.3%
o 99936
 
5.3%
l 99867
 
5.3%
Other values (9) 366744
19.3%

Account_Balance
Real number (ℝ)

Distinct197954
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52437.989
Minimum5000.82
Maximum99999.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-02-20T04:44:30.656036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5000.82
5-th percentile9736.289
Q128742.395
median52372.555
Q376147.67
95-th percentile95230.17
Maximum99999.95
Range94999.13
Interquartile range (IQR)47405.275

Descriptive statistics

Standard deviation27399.507
Coefficient of variation (CV)0.52251255
Kurtosis-1.1976516
Mean52437.989
Median Absolute Deviation (MAD)23707.115
Skewness0.0038724569
Sum1.0487598 × 1010
Variance7.5073299 × 108
MonotonicityNot monotonic
2025-02-20T04:44:30.906438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43230.74 3
 
< 0.1%
10338.39 3
 
< 0.1%
13998.58 3
 
< 0.1%
83952.51 3
 
< 0.1%
76084.34 3
 
< 0.1%
20817.66 3
 
< 0.1%
85438.21 3
 
< 0.1%
33235.53 3
 
< 0.1%
38734.58 3
 
< 0.1%
98929.64 3
 
< 0.1%
Other values (197944) 199970
> 99.9%
ValueCountFrequency (%)
5000.82 1
< 0.1%
5000.93 1
< 0.1%
5001.08 1
< 0.1%
5001.12 1
< 0.1%
5002.27 1
< 0.1%
5002.71 1
< 0.1%
5003.41 1
< 0.1%
5003.42 1
< 0.1%
5003.5 1
< 0.1%
5003.68 1
< 0.1%
ValueCountFrequency (%)
99999.95 1
< 0.1%
99998.68 1
< 0.1%
99997.65 1
< 0.1%
99997.64 1
< 0.1%
99997.28 1
< 0.1%
99996.44 1
< 0.1%
99996.03 1
< 0.1%
99995.54 1
< 0.1%
99995.49 1
< 0.1%
99995.21 1
< 0.1%
Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
Self-service Banking Machine
21707 
ATM
21200 
ATM Booth Kiosk
21149 
Debit/Credit Card
 
8273
Smart Card
 
8133
Other values (15)
119538 

Length

Max length28
Median length15
Mean length14.377135
Min length3

Characters and Unicode

Total characters2875427
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVoice Assistant
2nd rowPOS Mobile Device
3rd rowATM
4th rowPOS Mobile App
5th rowVirtual Card

Common Values

ValueCountFrequency (%)
Self-service Banking Machine 21707
 
10.9%
ATM 21200
 
10.6%
ATM Booth Kiosk 21149
 
10.6%
Debit/Credit Card 8273
 
4.1%
Smart Card 8133
 
4.1%
Wearable Device 8128
 
4.1%
Virtual Card 8059
 
4.0%
Tablet 8059
 
4.0%
Desktop/Laptop 8057
 
4.0%
Voice Assistant 8039
 
4.0%
Other values (10) 79196
39.6%

Length

2025-02-20T04:44:31.556576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
atm 42349
 
9.7%
device 31887
 
7.3%
banking 29702
 
6.8%
card 24465
 
5.6%
mobile 23753
 
5.4%
pos 23722
 
5.4%
self-service 21707
 
5.0%
machine 21707
 
5.0%
kiosk 21149
 
4.8%
booth 21149
 
4.8%
Other values (21) 175636
40.2%

Most occurring characters

ValueCountFrequency (%)
e 302650
 
10.5%
237226
 
8.3%
i 214340
 
7.5%
a 203542
 
7.1%
n 152362
 
5.3%
o 143219
 
5.0%
r 134272
 
4.7%
t 133828
 
4.7%
c 115037
 
4.0%
M 87809
 
3.1%
Other values (31) 1151142
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2875427
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 302650
 
10.5%
237226
 
8.3%
i 214340
 
7.5%
a 203542
 
7.1%
n 152362
 
5.3%
o 143219
 
5.0%
r 134272
 
4.7%
t 133828
 
4.7%
c 115037
 
4.0%
M 87809
 
3.1%
Other values (31) 1151142
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2875427
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 302650
 
10.5%
237226
 
8.3%
i 214340
 
7.5%
a 203542
 
7.1%
n 152362
 
5.3%
o 143219
 
5.0%
r 134272
 
4.7%
t 133828
 
4.7%
c 115037
 
4.0%
M 87809
 
3.1%
Other values (31) 1151142
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2875427
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 302650
 
10.5%
237226
 
8.3%
i 214340
 
7.5%
a 203542
 
7.1%
n 152362
 
5.3%
o 143219
 
5.0%
r 134272
 
4.7%
t 133828
 
4.7%
c 115037
 
4.0%
M 87809
 
3.1%
Other values (31) 1151142
40.0%
Distinct148
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-02-20T04:44:31.836759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length39
Mean length20.07874
Min length11

Characters and Unicode

Total characters4015748
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThiruvananthapuram, Kerala
2nd rowNashik, Maharashtra
3rd rowBhagalpur, Bihar
4th rowChennai, Tamil Nadu
5th rowAmritsar, Punjab
ValueCountFrequency (%)
pradesh 29497
 
5.8%
and 23403
 
4.6%
chandigarh 13932
 
2.7%
delhi 11886
 
2.3%
daman 7879
 
1.5%
nicobar 7788
 
1.5%
diu 7776
 
1.5%
puducherry 7232
 
1.4%
west 7043
 
1.4%
nagaland 6031
 
1.2%
Other values (177) 390034
76.1%
2025-02-20T04:44:32.880979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 740094
18.4%
312501
 
7.8%
r 294392
 
7.3%
h 238835
 
5.9%
n 202566
 
5.0%
, 200000
 
5.0%
i 195274
 
4.9%
d 171813
 
4.3%
e 130970
 
3.3%
u 129968
 
3.2%
Other values (40) 1399335
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4015748
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 740094
18.4%
312501
 
7.8%
r 294392
 
7.3%
h 238835
 
5.9%
n 202566
 
5.0%
, 200000
 
5.0%
i 195274
 
4.9%
d 171813
 
4.3%
e 130970
 
3.3%
u 129968
 
3.2%
Other values (40) 1399335
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4015748
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 740094
18.4%
312501
 
7.8%
r 294392
 
7.3%
h 238835
 
5.9%
n 202566
 
5.0%
, 200000
 
5.0%
i 195274
 
4.9%
d 171813
 
4.3%
e 130970
 
3.3%
u 129968
 
3.2%
Other values (40) 1399335
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4015748
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 740094
18.4%
312501
 
7.8%
r 294392
 
7.3%
h 238835
 
5.9%
n 202566
 
5.0%
, 200000
 
5.0%
i 195274
 
4.9%
d 171813
 
4.3%
e 130970
 
3.3%
u 129968
 
3.2%
Other values (40) 1399335
34.8%

Device_Type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.8 MiB
POS
50111 
ATM
50055 
Mobile
49962 
Desktop
49872 

Length

Max length7
Median length3
Mean length4.74687
Min length3

Characters and Unicode

Total characters949374
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOS
2nd rowDesktop
3rd rowDesktop
4th rowMobile
5th rowMobile

Common Values

ValueCountFrequency (%)
POS 50111
25.1%
ATM 50055
25.0%
Mobile 49962
25.0%
Desktop 49872
24.9%

Length

2025-02-20T04:44:33.337826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T04:44:33.674333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pos 50111
25.1%
atm 50055
25.0%
mobile 49962
25.0%
desktop 49872
24.9%

Most occurring characters

ValueCountFrequency (%)
M 100017
 
10.5%
o 99834
 
10.5%
e 99834
 
10.5%
P 50111
 
5.3%
O 50111
 
5.3%
S 50111
 
5.3%
A 50055
 
5.3%
T 50055
 
5.3%
b 49962
 
5.3%
i 49962
 
5.3%
Other values (6) 299322
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 949374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 100017
 
10.5%
o 99834
 
10.5%
e 99834
 
10.5%
P 50111
 
5.3%
O 50111
 
5.3%
S 50111
 
5.3%
A 50055
 
5.3%
T 50055
 
5.3%
b 49962
 
5.3%
i 49962
 
5.3%
Other values (6) 299322
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 949374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 100017
 
10.5%
o 99834
 
10.5%
e 99834
 
10.5%
P 50111
 
5.3%
O 50111
 
5.3%
S 50111
 
5.3%
A 50055
 
5.3%
T 50055
 
5.3%
b 49962
 
5.3%
i 49962
 
5.3%
Other values (6) 299322
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 949374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 100017
 
10.5%
o 99834
 
10.5%
e 99834
 
10.5%
P 50111
 
5.3%
O 50111
 
5.3%
S 50111
 
5.3%
A 50055
 
5.3%
T 50055
 
5.3%
b 49962
 
5.3%
i 49962
 
5.3%
Other values (6) 299322
31.5%

Is_Fraud
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.1 MiB
0
189912 
1
 
10088

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters200000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 189912
95.0%
1 10088
 
5.0%

Length

2025-02-20T04:44:34.472268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T04:44:34.699883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 189912
95.0%
1 10088
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 189912
95.0%
1 10088
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 189912
95.0%
1 10088
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 189912
95.0%
1 10088
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 189912
95.0%
1 10088
 
5.0%

Transaction_Currency
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.4 MiB
INR
200000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters600000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINR
2nd rowINR
3rd rowINR
4th rowINR
5th rowINR

Common Values

ValueCountFrequency (%)
INR 200000
100.0%

Length

2025-02-20T04:44:34.936114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T04:44:35.172578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inr 200000
100.0%

Most occurring characters

ValueCountFrequency (%)
I 200000
33.3%
N 200000
33.3%
R 200000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 600000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 200000
33.3%
N 200000
33.3%
R 200000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 600000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 200000
33.3%
N 200000
33.3%
R 200000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 600000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 200000
33.3%
N 200000
33.3%
R 200000
33.3%
Distinct9000
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size13.5 MiB
2025-02-20T04:44:35.549153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters2800000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row+9198579XXXXXX
2nd row+9191074XXXXXX
3rd row+9197745XXXXXX
4th row+9195889XXXXXX
5th row+9195316XXXXXX
ValueCountFrequency (%)
9191471xxxxxx 41
 
< 0.1%
9191943xxxxxx 40
 
< 0.1%
9198593xxxxxx 39
 
< 0.1%
9194008xxxxxx 39
 
< 0.1%
9197078xxxxxx 39
 
< 0.1%
9196811xxxxxx 38
 
< 0.1%
9194655xxxxxx 38
 
< 0.1%
9199538xxxxxx 38
 
< 0.1%
9199576xxxxxx 37
 
< 0.1%
9194187xxxxxx 37
 
< 0.1%
Other values (8990) 199614
99.8%
2025-02-20T04:44:36.220232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
X 1200000
42.9%
9 482579
17.2%
1 282399
 
10.1%
+ 200000
 
7.1%
5 82648
 
3.0%
7 82440
 
2.9%
2 82357
 
2.9%
6 82153
 
2.9%
3 81881
 
2.9%
8 81861
 
2.9%
Other values (2) 141682
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2800000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 1200000
42.9%
9 482579
17.2%
1 282399
 
10.1%
+ 200000
 
7.1%
5 82648
 
3.0%
7 82440
 
2.9%
2 82357
 
2.9%
6 82153
 
2.9%
3 81881
 
2.9%
8 81861
 
2.9%
Other values (2) 141682
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2800000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 1200000
42.9%
9 482579
17.2%
1 282399
 
10.1%
+ 200000
 
7.1%
5 82648
 
3.0%
7 82440
 
2.9%
2 82357
 
2.9%
6 82153
 
2.9%
3 81881
 
2.9%
8 81861
 
2.9%
Other values (2) 141682
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2800000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 1200000
42.9%
9 482579
17.2%
1 282399
 
10.1%
+ 200000
 
7.1%
5 82648
 
3.0%
7 82440
 
2.9%
2 82357
 
2.9%
6 82153
 
2.9%
3 81881
 
2.9%
8 81861
 
2.9%
Other values (2) 141682
 
5.1%
Distinct172
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
2025-02-20T04:44:37.101166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length23
Mean length17.161245
Min length8

Characters and Unicode

Total characters3432249
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBitcoin transaction
2nd rowGrocery delivery
3rd rowMutual fund investment
4th rowFood delivery
5th rowDebt repayment
ValueCountFrequency (%)
payment 30251
 
6.5%
purchase 25725
 
5.6%
online 11497
 
2.5%
subscription 11468
 
2.5%
fee 10406
 
2.2%
service 10386
 
2.2%
shopping 9336
 
2.0%
home 7015
 
1.5%
gift 7006
 
1.5%
ticket 5867
 
1.3%
Other values (199) 333855
72.1%
2025-02-20T04:44:37.981040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 368338
 
10.7%
n 265521
 
7.7%
262812
 
7.7%
r 247707
 
7.2%
i 244217
 
7.1%
t 238930
 
7.0%
a 234004
 
6.8%
s 175747
 
5.1%
o 168746
 
4.9%
c 148770
 
4.3%
Other values (39) 1077457
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3432249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 368338
 
10.7%
n 265521
 
7.7%
262812
 
7.7%
r 247707
 
7.2%
i 244217
 
7.1%
t 238930
 
7.0%
a 234004
 
6.8%
s 175747
 
5.1%
o 168746
 
4.9%
c 148770
 
4.3%
Other values (39) 1077457
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3432249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 368338
 
10.7%
n 265521
 
7.7%
262812
 
7.7%
r 247707
 
7.2%
i 244217
 
7.1%
t 238930
 
7.0%
a 234004
 
6.8%
s 175747
 
5.1%
o 168746
 
4.9%
c 148770
 
4.3%
Other values (39) 1077457
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3432249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 368338
 
10.7%
n 265521
 
7.7%
262812
 
7.7%
r 247707
 
7.2%
i 244217
 
7.1%
t 238930
 
7.0%
a 234004
 
6.8%
s 175747
 
5.1%
o 168746
 
4.9%
c 148770
 
4.3%
Other values (39) 1077457
31.4%
Distinct4779
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size14.9 MiB
2025-02-20T04:44:38.669642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length26
Mean length20.923715
Min length15

Characters and Unicode

Total characters4184743
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowoshaXXXXX@XXXXX.com
2nd rowhredhaanXXXX@XXXXXX.com
3rd rowekaniXXX@XXXXXX.com
4th rowyaminiXXXXX@XXXXXXX.com
5th rowkritikaXXXX@XXXXXX.com
ValueCountFrequency (%)
krishnaxxx@xxxxx.com 99
 
< 0.1%
saixxxxx@xxxxxx.com 95
 
< 0.1%
saixxxxx@xxxxxxx.com 93
 
< 0.1%
saixxx@xxxxxx.com 92
 
< 0.1%
saixxxx@xxxxxxx.com 92
 
< 0.1%
saixxxx@xxxxxx.com 92
 
< 0.1%
krishnaxxxx@xxxxxxx.com 91
 
< 0.1%
krishnaxxxx@xxxxx.com 88
 
< 0.1%
saixxxxx@xxxxx.com 80
 
< 0.1%
saixxx@xxxxx.com 79
 
< 0.1%
Other values (4769) 199099
99.5%
2025-02-20T04:44:39.526585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
X 1999767
47.8%
a 270349
 
6.5%
m 235269
 
5.6%
o 221263
 
5.3%
c 219224
 
5.2%
@ 200000
 
4.8%
. 200000
 
4.8%
i 128668
 
3.1%
h 90677
 
2.2%
n 87176
 
2.1%
Other values (19) 532350
 
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4184743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 1999767
47.8%
a 270349
 
6.5%
m 235269
 
5.6%
o 221263
 
5.3%
c 219224
 
5.2%
@ 200000
 
4.8%
. 200000
 
4.8%
i 128668
 
3.1%
h 90677
 
2.2%
n 87176
 
2.1%
Other values (19) 532350
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4184743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 1999767
47.8%
a 270349
 
6.5%
m 235269
 
5.6%
o 221263
 
5.3%
c 219224
 
5.2%
@ 200000
 
4.8%
. 200000
 
4.8%
i 128668
 
3.1%
h 90677
 
2.2%
n 87176
 
2.1%
Other values (19) 532350
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4184743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 1999767
47.8%
a 270349
 
6.5%
m 235269
 
5.6%
o 221263
 
5.3%
c 219224
 
5.2%
@ 200000
 
4.8%
. 200000
 
4.8%
i 128668
 
3.1%
h 90677
 
2.2%
n 87176
 
2.1%
Other values (19) 532350
 
12.7%

Transaction_year
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
2025
200000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters800000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2025
2nd row2025
3rd row2025
4th row2025
5th row2025

Common Values

ValueCountFrequency (%)
2025 200000
100.0%

Length

2025-02-20T04:44:39.674605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T04:44:39.760725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2025 200000
100.0%

Most occurring characters

ValueCountFrequency (%)
2 400000
50.0%
0 200000
25.0%
5 200000
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 800000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 400000
50.0%
0 200000
25.0%
5 200000
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 800000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 400000
50.0%
0 200000
25.0%
5 200000
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 800000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 400000
50.0%
0 200000
25.0%
5 200000
25.0%

Transaction_month
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 MiB
January
200000 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1400000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJanuary
2nd rowJanuary
3rd rowJanuary
4th rowJanuary
5th rowJanuary

Common Values

ValueCountFrequency (%)
January 200000
100.0%

Length

2025-02-20T04:44:39.859237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T04:44:39.941976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
january 200000
100.0%

Most occurring characters

ValueCountFrequency (%)
a 400000
28.6%
J 200000
14.3%
n 200000
14.3%
u 200000
14.3%
r 200000
14.3%
y 200000
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1400000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 400000
28.6%
J 200000
14.3%
n 200000
14.3%
u 200000
14.3%
r 200000
14.3%
y 200000
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1400000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 400000
28.6%
J 200000
14.3%
n 200000
14.3%
u 200000
14.3%
r 200000
14.3%
y 200000
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1400000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 400000
28.6%
J 200000
14.3%
n 200000
14.3%
u 200000
14.3%
r 200000
14.3%
y 200000
14.3%

Transaction_day
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
Thursday
33581 
Wednesday
33340 
Saturday
26884 
Sunday
26711 
Monday
26557 
Other values (2)
52927 

Length

Max length9
Median length8
Mean length7.236925
Min length6

Characters and Unicode

Total characters1447385
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowSaturday
3rd rowSaturday
4th rowSunday
5th rowThursday

Common Values

ValueCountFrequency (%)
Thursday 33581
16.8%
Wednesday 33340
16.7%
Saturday 26884
13.4%
Sunday 26711
13.4%
Monday 26557
13.3%
Friday 26492
13.2%
Tuesday 26435
13.2%

Length

2025-02-20T04:44:40.064925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T04:44:40.205075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
thursday 33581
16.8%
wednesday 33340
16.7%
saturday 26884
13.4%
sunday 26711
13.4%
monday 26557
13.3%
friday 26492
13.2%
tuesday 26435
13.2%

Most occurring characters

ValueCountFrequency (%)
d 233340
16.1%
a 226884
15.7%
y 200000
13.8%
u 113611
7.8%
s 93356
6.4%
e 93115
 
6.4%
r 86957
 
6.0%
n 86608
 
6.0%
T 60016
 
4.1%
S 53595
 
3.7%
Other values (7) 199903
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1447385
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 233340
16.1%
a 226884
15.7%
y 200000
13.8%
u 113611
7.8%
s 93356
6.4%
e 93115
 
6.4%
r 86957
 
6.0%
n 86608
 
6.0%
T 60016
 
4.1%
S 53595
 
3.7%
Other values (7) 199903
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1447385
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 233340
16.1%
a 226884
15.7%
y 200000
13.8%
u 113611
7.8%
s 93356
6.4%
e 93115
 
6.4%
r 86957
 
6.0%
n 86608
 
6.0%
T 60016
 
4.1%
S 53595
 
3.7%
Other values (7) 199903
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1447385
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 233340
16.1%
a 226884
15.7%
y 200000
13.8%
u 113611
7.8%
s 93356
6.4%
e 93115
 
6.4%
r 86957
 
6.0%
n 86608
 
6.0%
T 60016
 
4.1%
S 53595
 
3.7%
Other values (7) 199903
13.8%

Interactions

2025-02-20T04:44:17.595225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T04:44:16.546070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T04:44:17.075787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T04:44:17.794641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T04:44:16.731707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T04:44:17.245297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T04:44:17.968050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T04:44:16.906117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T04:44:17.417828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-20T04:44:40.375085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Account_BalanceAccount_TypeAgeDevice_TypeGenderIs_FraudMerchant_CategoryStateTransaction_AmountTransaction_DeviceTransaction_TimeTransaction_TypeTransaction_day
Account_Balance1.0000.0000.0000.0000.0000.0050.0030.000-0.0020.0000.0040.0040.002
Account_Type0.0001.0000.0000.0020.0000.0030.0000.0000.0000.0020.0000.0000.004
Age0.0000.0001.0000.0010.0000.0000.0000.000-0.0030.0000.0000.0000.000
Device_Type0.0000.0020.0011.0000.0000.0000.0000.0060.0050.0030.0000.0020.004
Gender0.0000.0000.0000.0001.0000.0000.0000.0040.0000.0000.0000.0040.002
Is_Fraud0.0050.0030.0000.0000.0001.0000.0030.0000.0030.0010.0000.0010.003
Merchant_Category0.0030.0000.0000.0000.0000.0031.0000.0000.0000.0040.0030.0010.000
State0.0000.0000.0000.0060.0040.0000.0001.0000.0000.0010.0040.0000.000
Transaction_Amount-0.0020.000-0.0030.0050.0000.0030.0000.0001.0000.0040.0000.0020.000
Transaction_Device0.0000.0020.0000.0030.0000.0010.0040.0010.0041.0000.0030.3630.003
Transaction_Time0.0040.0000.0000.0000.0000.0000.0030.0040.0000.0031.0000.0000.000
Transaction_Type0.0040.0000.0000.0020.0040.0010.0010.0000.0020.3630.0001.0000.000
Transaction_day0.0020.0040.0000.0040.0020.0030.0000.0000.0000.0030.0000.0001.000

Missing values

2025-02-20T04:44:18.485095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-20T04:44:19.428383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Customer_IDCustomer_NameGenderAgeStateCityBank_BranchAccount_TypeTransaction_IDTransaction_DateTransaction_TimeTransaction_AmountMerchant_IDTransaction_TypeMerchant_CategoryAccount_BalanceTransaction_DeviceTransaction_LocationDevice_TypeIs_FraudTransaction_CurrencyCustomer_ContactTransaction_DescriptionCustomer_EmailTransaction_yearTransaction_monthTransaction_day
0d5f6ec07-d69e-4f47-b9b4-7c58ff17c19eOsha TellaMale60KeralaThiruvananthapuramThiruvananthapuram BranchSavings4fa3208f-9e23-42dc-b330-844829d0c12c2025-01-23Afternoon32415.45214e03c5-5c34-40d1-a66c-f440aa2bbd02TransferRestaurant74557.27Voice AssistantThiruvananthapuram, KeralaPOS0INR+9198579XXXXXXBitcoin transactionoshaXXXXX@XXXXX.com2025JanuaryThursday
17c14ad51-781a-4db9-b7bd-67439c175262Hredhaan KhoslaFemale51MaharashtraNashikNashik BranchBusinessc9de0c06-2c4c-40a9-97ed-3c7b8f97c79c2025-01-11Evening43622.60f9e3f11f-28d3-4199-b0ca-f225a155ede6Bill PaymentRestaurant74622.66POS Mobile DeviceNashik, MaharashtraDesktop0INR+9191074XXXXXXGrocery deliveryhredhaanXXXX@XXXXXX.com2025JanuarySaturday
23a73a0e5-d4da-45aa-85f3-528413900a35Ekani NazarethMale20BiharBhagalpurBhagalpur BranchSavingse41c55f9-c016-4ff3-872b-cae72467c75c2025-01-25Night63062.5697977d83-5486-4510-af1c-8dada3e1cfa0Bill PaymentGroceries66817.99ATMBhagalpur, BiharDesktop0INR+9197745XXXXXXMutual fund investmentekaniXXX@XXXXXX.com2025JanuarySaturday
37902f4ef-9050-4a79-857d-9c2ea3181940Yamini RamachandranFemale57Tamil NaduChennaiChennai BranchBusiness7f7ee11b-ff2c-45a3-802a-49bc47c02ecb2025-01-19Afternoon14000.72f45cd6b3-5092-44d0-8afb-490894605184DebitEntertainment58177.08POS Mobile AppChennai, Tamil NaduMobile0INR+9195889XXXXXXFood deliveryyaminiXXXXX@XXXXXXX.com2025JanuarySunday
43a4bba70-d9a9-4c5f-8b92-1735fd8c19e9Kritika RegeFemale43PunjabAmritsarAmritsar BranchSavingsf8e6ac6f-81a1-4985-bf12-f60967d852ef2025-01-30Evening18335.1670dd77dd-3b00-4b2c-8ebc-cfb8af5f6741TransferEntertainment16108.56Virtual CardAmritsar, PunjabMobile0INR+9195316XXXXXXDebt repaymentkritikaXXXX@XXXXXX.com2025JanuaryThursday
56c870d65-76b0-431d-bdf3-9292998e8211Ishanvi DarMale54GujaratAhmedabadAhmedabad BranchCheckingaf5f667c-d064-4083-bfb7-83396111a3da2025-01-25Morning9711.15d82a798d-7b4d-4609-a687-8f6b5fc58fe7TransferEntertainment61258.85Mobile DeviceAhmedabad, GujaratPOS0INR+9198318XXXXXXSeminar registrationishanviXXX@XXXXX.com2025JanuarySaturday
65323737c-bbd2-423f-9c9b-e0433c8f75dcArya ShroffFemale61DelhiNew DelhiNew Delhi BranchBusinessb1355810-d246-4aeb-9932-347f326461722025-01-04Night94677.01e0b802ba-b1d9-4e94-9a88-6fbb6066b4e0TransferHealth36313.61Payment Gateway DeviceNew Delhi, DelhiDesktop0INR+9194785XXXXXXPublic transport passaryaXXX@XXXXX.com2025JanuarySaturday
7c0c3d474-f6c2-4c66-9b0e-f9ba75c6f310Jackson ShereMale32Andaman and Nicobar IslandsPort BlairPort Blair BranchBusinessc86a000c-d81f-40be-acdf-77fc072fd8082025-01-16Night67704.28ec344461-1bab-4e85-a1cb-05061e9149bcDebitClothing16948.73Debit/Credit CardPort Blair, Andaman and Nicobar IslandsATM0INR+9193423XXXXXXOnline shoppingjacksonXXX@XXXXXXX.com2025JanuaryThursday
8e9a82764-1253-4a46-ad34-80e3416fc801Bhanumati RavelMale52Madhya PradeshBhopalBhopal BranchBusiness323cc683-b0dc-40ee-a519-3b5dc96c7ed82025-01-25Morning72953.451de36b2e-e1a9-4e29-b8f8-8693919f0bb1WithdrawalClothing18138.71ATMBhopal, Madhya PradeshMobile0INR+9194374XXXXXXVacation paymentbhanumatiXXXXX@XXXXX.com2025JanuarySaturday
9708224d5-192a-4d86-b411-8ec6d1bb274bMeera GaneshFemale32ChhattisgarhJagdalpurJagdalpur BranchBusiness9fad31ea-2770-4d80-a0ea-00972d5f02cc2025-01-02Night5689.0220ffaa8f-958b-4848-b2e4-f62a3fda9bc1CreditEntertainment65801.35Bank BranchJagdalpur, ChhattisgarhDesktop0INR+9194511XXXXXXElectronics purchasemeeraXXXXX@XXXXXXX.com2025JanuaryThursday
Customer_IDCustomer_NameGenderAgeStateCityBank_BranchAccount_TypeTransaction_IDTransaction_DateTransaction_TimeTransaction_AmountMerchant_IDTransaction_TypeMerchant_CategoryAccount_BalanceTransaction_DeviceTransaction_LocationDevice_TypeIs_FraudTransaction_CurrencyCustomer_ContactTransaction_DescriptionCustomer_EmailTransaction_yearTransaction_monthTransaction_day
199990602e081e-789f-4438-b392-f883763f2878Bhavya PrabhuFemale64Andhra PradeshVijayawadaVijayawada BranchCheckingf6e59b3a-c129-47cf-a19d-10b5a96d90df2025-01-17Morning86350.15810c7e5c-23e7-435b-990e-0603f370c22bBill PaymentElectronics13673.68Voice AssistantVijayawada, Andhra PradeshMobile0INR+9198868XXXXXXCryptocurrency purchasebhavyaXXXXX@XXXXX.com2025JanuaryFriday
199991f1116e1e-7e9e-4d79-8680-443362cddf02Vivaan KeerMale46AssamNagaonNagaon BranchCheckingc987c776-e480-46d5-94ac-8c3c282035272025-01-18Afternoon82034.24351bd768-876d-45df-acce-7c46414f1c93DebitElectronics15367.42POS TerminalNagaon, AssamATM0INR+9192194XXXXXXOnline software purchasevivaanXXX@XXXXX.com2025JanuarySaturday
199992e4f07df0-07e4-4f1a-a6be-0e8cd8fdbf03Ayush SachdevMale55Madhya PradeshIndoreIndore BranchBusinessb1b7ea26-2165-43be-a804-6ae8d37f000d2025-01-17Afternoon30508.30676f4516-c9c3-408a-8c68-e3bee3ccf309TransferGroceries57713.49Self-service Banking MachineIndore, Madhya PradeshDesktop0INR+9193108XXXXXXCredit card paymentayushXXXXX@XXXXX.com2025JanuaryFriday
199993970f2cee-92cb-4416-9cfe-1544cf0a56b4Aahana MohanFemale21RajasthanJaipurJaipur BranchSavingse4ecb6df-8054-4cca-9b46-b4390fc1ff282025-01-28Night48301.18e8ffaed3-0bf3-4f82-af34-24e6129a0651WithdrawalRestaurant19935.50ATMJaipur, RajasthanATM0INR+9194394XXXXXXMovie ticketsaahanaXXXX@XXXXXXX.com2025JanuaryTuesday
19999495a2104c-8634-4f21-9ce2-6caef57037d6William BalaMale56Dadra and Nagar Haveli and Daman and DiuSilvassaSilvassa BranchChecking6215e64f-5b2c-41ca-8f0c-d7c2cea8e4fd2025-01-24Morning96247.88cc24468f-0c91-46aa-b6c9-b6f3fb1cf66cTransferHealth34423.94Smart CardSilvassa, Dadra and Nagar Haveli and Daman and DiuPOS0INR+9198166XXXXXXMembership subscriptionwilliamXXXXX@XXXXXX.com2025JanuaryFriday
199995b8bdae19-296f-48b7-9104-e055d33a09acVedhika MagarFemale55Dadra and Nagar Haveli and Daman and DiuDiuDiu BranchBusiness8d856bc7-4666-4509-a067-48d67500694a2025-01-08Evening98513.7472817d4a-830b-4d16-bf74-244dccfe4cc4CreditRestaurant37475.11Desktop/LaptopDiu, Dadra and Nagar Haveli and Daman and DiuATM0INR+9192629XXXXXXATM withdrawalvedhikaXXXXX@XXXXXXX.com2025JanuaryWednesday
199996635bc099-8a93-48ee-829a-bf2283fe8fdaAashi PaiMale51ManipurKangpokpiKangpokpi BranchBusinessf2890dbd-4e01-445d-97f5-ac56886e90372025-01-01Evening40593.55087718f9-1faa-44ef-b162-24d20ddc903cWithdrawalGroceries53037.20ATMKangpokpi, ManipurATM0INR+9198116XXXXXXSubscription boxaashiXXX@XXXXXX.com2025JanuaryWednesday
199997c1b31cc2-0905-47e8-8cc5-6461d1f3ba33Dayita ShanFemale41ChandigarhChandigarhChandigarh BranchSavingsf714b758-7539-474d-b676-5fa7e24801412025-01-28Morning61579.70a313cfbc-ef3d-4e59-8347-a948ac292a6fWithdrawalHealth96225.36ATMChandigarh, ChandigarhDesktop0INR+9192601XXXXXXCharity donationdayitaXXXX@XXXXXXX.com2025JanuaryTuesday
19999832bb8e66-f3fa-43bf-8242-dab9a6116310Unnati VyasFemale28TelanganaNizamabadNizamabad BranchCheckingf6903b6a-b582-47ea-95d5-aff16bdec9502025-01-08Morning39488.2208ef8813-dea0-42bf-9df5-0a63fe07673dDebitElectronics89599.90Voice AssistantNizamabad, TelanganaDesktop0INR+9197537XXXXXXTourist attraction paymentunnatiXXX@XXXXX.com2025JanuaryWednesday
199999f3dd92ef-b17a-45d2-b6cb-fdbee20843feGopal RoutMale34NagalandKohimaKohima BranchBusinessb44b6e8a-1036-4ec1-b492-5e7ffc7baf6d2025-01-08Afternoon58622.49e0d4aa67-43c0-4aed-836b-698cfaf2df41DebitElectronics15066.24Virtual CardKohima, NagalandMobile0INR+9193961XXXXXXInstallment paymentgopalXXXXX@XXXXXXX.com2025JanuaryWednesday